It’s all about trade-offs. Here are a few reasons why one might care about performance in their Python code:
Performance is often more tied to the code than to the interpreter - an O(n³) algorithm in blazing fast C won’t necessarily perform any better than an O(nlogn) algorithm in Python.
Just because this particular Python code isn’t particularly performance constrained doesn’t mean you’re okay with it taking twice as long.
Rewriting a large code base can be very expensive and error-prone. Converting small, very performance-sensitive parts of the code to a compiled language while keeping the bulk of the business logic in Python is often a much better value proposition.
These are also performance benefits one can get essentially for free with linter rules.
Anecdotally: in my final year of university I took a computational physics class. Many of my classmates wrote their simulations in C or C++. I would rotate between Matlab, Octave and Python. During one of our labs where we wrote particle simulations, I wrote and ran Octave and Python simulations in the time it took my classmates to write their C/C++ versions, and the two fastest simulations in the class were my Octave and Python ones, respectively. (The professor’s own sim came in third place). The overhead my classmates had dealing with poorly optimised code that caused constant cache misses was far greater than the interpreter overhead in my code (though at the time I don’t think I could have explained why their code was so slow compared to mine).
I appreciate the large amount of info. Great answer. It just doesn’t make sense to me, all things being equal (including performant algorithms), why choose Python and then make a small performance tweak like in the article? I understand preferring the faster implementation, but it seems to me like waxing your car to reduce wind resistance to make it go faster, when installing a turbo-charger would be much more effective.
If you use the profiler and see that the slower operation is being used frequently, and is taking up a chunk of time deemed significant, why not swap it to the faster version?
In a simulation I’m working on that goes through 42 million rounds I spent some time profiling and going through the code that was eating up a lot of time (especially things executed all 42 million times) and trying to find some optimizations. Brought the run time down from about 10 minutes to 5 minutes.
I certainly wasn’t going to start over in C++ or Rust, and if I’d started with either of those languages I would have missed out on a lot of really strong Python libraries and probably spent more time coding rather than refining the simulation.
I think a better analogy would be that you’re tuning your bike for better performance because the trade-offs of switching to a car are worse than keeping the bike.
If anything, to me it seems more important for a slower language to be optimized. Ideally everything would be perfectly optimized, but over-optimization is a thing: making optimizations that aren’t economical. Even though c is many times faster than python, for many projects it’s fast enough that it makes no practical difference to the user. They’re not going to bitch about a function taking 0.1 seconds to execute instead of 0.001, but they might start to care when that becomes 100 seconds vs 1. As the program becomes more time intensive to run, the python code is going to hit that threshold where the user starts to notice before c, so economically, the python would need to be optimized first.
It is a small performance tweak if done once, right? But let’s suppose people worried about refactoring here would have checked to see what areas of their code are seeing heavy use.
Performance is often more tied to the code than to the interpreter - an O(n³) algorithm in blazing fast C won’t necessarily perform any better than an O(nlogn) algorithm in Python.
An O(n³) algorithm in Python won’t necessarily perform any better than an O(nlogn) algorithm in C. Ever heard of galactic algorithms?
The overhead my classmates had dealing with poorly optimised code that caused constant cache misses was far greater than the interpreter overhead in my code (though at the time I don’t think I could have explained why their code was so slow compared to mine).
It’s all about trade-offs. Here are a few reasons why one might care about performance in their Python code:
These are also performance benefits one can get essentially for free with linter rules.
Anecdotally: in my final year of university I took a computational physics class. Many of my classmates wrote their simulations in C or C++. I would rotate between Matlab, Octave and Python. During one of our labs where we wrote particle simulations, I wrote and ran Octave and Python simulations in the time it took my classmates to write their C/C++ versions, and the two fastest simulations in the class were my Octave and Python ones, respectively. (The professor’s own sim came in third place). The overhead my classmates had dealing with poorly optimised code that caused constant cache misses was far greater than the interpreter overhead in my code (though at the time I don’t think I could have explained why their code was so slow compared to mine).
I appreciate the large amount of info. Great answer. It just doesn’t make sense to me, all things being equal (including performant algorithms), why choose Python and then make a small performance tweak like in the article? I understand preferring the faster implementation, but it seems to me like waxing your car to reduce wind resistance to make it go faster, when installing a turbo-charger would be much more effective.
If you use the profiler and see that the slower operation is being used frequently, and is taking up a chunk of time deemed significant, why not swap it to the faster version?
In a simulation I’m working on that goes through 42 million rounds I spent some time profiling and going through the code that was eating up a lot of time (especially things executed all 42 million times) and trying to find some optimizations. Brought the run time down from about 10 minutes to 5 minutes.
I certainly wasn’t going to start over in C++ or Rust, and if I’d started with either of those languages I would have missed out on a lot of really strong Python libraries and probably spent more time coding rather than refining the simulation.
I think a better analogy would be that you’re tuning your bike for better performance because the trade-offs of switching to a car are worse than keeping the bike.
Ok… I’ll buy that
Good, more people should buy bicycles
If anything, to me it seems more important for a slower language to be optimized. Ideally everything would be perfectly optimized, but over-optimization is a thing: making optimizations that aren’t economical. Even though c is many times faster than python, for many projects it’s fast enough that it makes no practical difference to the user. They’re not going to bitch about a function taking 0.1 seconds to execute instead of 0.001, but they might start to care when that becomes 100 seconds vs 1. As the program becomes more time intensive to run, the python code is going to hit that threshold where the user starts to notice before c, so economically, the python would need to be optimized first.
It is a small performance tweak if done once, right? But let’s suppose people worried about refactoring here would have checked to see what areas of their code are seeing heavy use.
An O(n³) algorithm in Python won’t necessarily perform any better than an O(nlogn) algorithm in C. Ever heard of galactic algorithms?
Did they write naive linear algebra operators?